Learning Techniques – Classification of Insulin-Dependent Diabetes Mellitus among adults C.LALITHA 1 P. ANBUMANI 2 1 Assistant Professor, Department of Computer Application, Tagore College of Arts and Science, Chrompet, Chennai-600 044.2 Research Scholar, Department of Computer Science, Periyar University, Salem – 636 011Abstract: Insulin-dependent diabetes mellitus (IDDM) is a set of correlated diseases in which the body cannot regulate the amount of sugar in the blood. It mainly affects the adult and characterized by chronic hyperglycemia associated with disturbances of carbohydrate, fat, and protein metabolism due to absolute or relative deficiency in insulin secretion and/or action. It causes long term damage, dysfunction and failure of various organs such as eyes, kidneys, nerves, heart and blood vessels.
A new methodology is used to find the stages of Insulin-dependent diabetes mellitus using Convolutional Neural Network (CNN). The symptoms and stages of Insulin-dependent diabetes mellitus are classified by using CNN technique. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers. If you have fully-connected layers at the end of your convolutional network, implementing dropout is easy. It helps us to know the various stages of Insulin-dependent diabetes mellitus and to predict the recommend preclusion to patients those who are affected by Insulin-dependent diabetes mellitus and provide implication to that patient.Index Terms — Convolutional neural network (CNN), Insulin-dependent diabetes mellitus (IDDM).I.
INTRODUCTIONInsulin-dependent diabetes mellitus is a disorder caused when the body doesn’t make enough insulin. This high blood sugar produces the classical symptoms of polyuria (frequent urination), polydipsia (increased thirst) and polyphagia (increased hunger).If left untreated, diabetes can lead to blindness, kidney disease, nerve disease, heart disease, and stroke.
Insulin-dependent diabetes mellitus often simply referred to as diabetes is a condition in which a person has high blood sugar, so that the body needs a special concern and provides extensive solutions to remain staying healthy, effervescent and lucid. This learning finds the knowledge by using supervised and unsupervised learning algorithms such as, Convolutional neural network. To find the efficiency of neural network technique by using the Root Mean square error and Mean absolute error 4 5. Beyond that the low level of attributes in Patient’s records falls in border phase of IDDM. Convolutional neural network has been employed to envisage the knowledge about the disease. Symptoms and stages are a class variables used for classification 19 20.
A network composed of more than one layer of neurons, with some or all of the outputs of each layer connected to one or more of the inputs of another layer. The first layer is called the input layer, the last one is the output layer, and in between there may be one or more hidden layers. IDDM – APPROACH Figure 1: Overall architecture diagramIn Phase I, the patient’s dataset has been collected from diabetologists. The patient’s dataset contains the symptoms of IDDM such as Excessive thirst, Fatigue, hayfever, Dry Skin, Blurred vision, sudden weight loss, tingling hands and feet etc. The information has been examined and used in this experiment. In this dataset, demographic information is gathered from the patients.
The result dataset contains the demographic and patient’s symptoms information is used for analysis 17 18.In Phase II and III, Predictive and Descriptive learning methods are used to find the impact of disease in the urban and rural area. The methods are one of the Convolutional neural network respectively and it is allocated to IDDM stages of the patients 15. The six sigmoid nodes are used as inputs, the weights are assigned to each node, and their six output layers are classified based on IDDM stages. According to the stages based classification, the ten sigmoid nodes are used as input and three output layers are classified based on the stages of IDDM 12. In Phase IV, the association between symptoms and IDDM stage is analyzed using motivating measures.II. REVIEW OF RELATED MODELING IMPETUS 2.
1 Hidden layer activation functionTo use a logistic (sigmoid) activation function for the hidden layers. A logistic function is recommended. Here Figure: 2 show the plot of a logistic activation function: Figure 2: Logistic Activation Function2.2 The Output Report Generated Using IDDM dataset2.2.1 Project Parameters============ Project Parameters ============Target variable: STAGES OF IDDMNumber of predictor variables: 9Type of model: convolutional Neural Network (CNN)Number of layers: 3 (1 hidden)Hidden layer activation function: LogisticOutput layer activation function: LogisticType of analysis: ClassificationCategory weights (priors): Data file distributionMisclassification costs: Specified cost matrixValidation method: Random sampling (90%) Input data file: J:IDDM documentsDATASET.
csvNumber of variables (data columns): 10Data sub setting: Use all data rowsNumber of data rows: 300The Project Parameters section of this IDDM report displays a summary of the options and parameters user can selected on the various property pages for the model. It reveals that the classification techniques are employed for finding the predicted knowledge using CNN. In this model, Nine predictor variable are used and one target for variable for prediction. Random sampling method is used for validating the data. Only three layers are used for finding the knowledge from this modeling using logistic activation. 2.
2.2 Summary of VariablesNo. Variables Class Type Missing Rows Categories1 Patients Predictor Categorical 0 302 Excessive thirst Predictor Continuous 0 123 Urinary Infection Predictor Continuous 0 24 Wrinkles Predictor Continuous 0 25 Skin problems Predictor Continuous 0 26 Fatigue Predictor Continuous 0 27 Hunger Predictor Continuous 0 28 Hayfever Predictor Continuous 0 29 yeast infections Predictor Continuous 0 210 Stages of IDDM Target Categorical 0 3 Table 1: Summary of Variables in IDDM datasets –Using DTREGThe Table 1 displays information about each variable in the IDDM dataset. The first column shows the name of the variable, the second column shows how the variable was used; the possibilities are Target, Predictor, Weight and Unused. The third column shows whether the variable is categorical or continuous, the forth column shows how many data rows had missing values on the variable, and the fifth column shows how many categories (discrete values) the variable has. In the case of continuous variables, the number of categories (such as Patients and Stages of IDDM) will be limited by the value specified for “Max.
Categories for predictor variables” on the model design property page.2.2.3 Classification Summary It reveals that misclassification does not exist in the validation. The cost and weight information are tabulated based on classification in Table 2 and 3. Category Actual category Misclassified category Percentage of cost Count weight Count weight percent costPrimary 99 99 0 0 0 0Nonsevere 63 63 0 0 0 0Severe 108 108 0 0 0 0total 270 270 0 0 0 0Table 2: Classification Table for Training DataCategory Actual category Misclassified category Percentage of cost Count weight Count weight percent costPrimary 11 11 0 0 0 0Nonsevere 7 7 0 0 0 0Severe 12 12 0 0 0 0Total 30 30 0 0 0 0Table 3: Classification Table for Validating DataIn this dataset, 30 objects are used for training and 270 objects are used for testing. Each category is classified and finds the weights are calculated using convolutional neural network.
2.2.4 Confusion Matrix TableA “Confusion Matrix” provides detailed information about how data rows are classified by the model.
The matrix has a row and column for each category of the target variable. The categories shown in the first column are the actual categories of the target variable. The categories shown across the top of the table are the predicted categories. The numbers in the cells are the weights of the data rows with the actual category of the row and the predicted category of the column. Here table 4 shows the IDDM datasets- confusion matrix. The numbers in the diagonal cells are the weights for the correctly classified cases where the actual category matches the predicted category. The off-diagonal cells have misclassified row weights. For IDDM dataset, the Non severe category was slightly misclassified as Primary and severe category.
Actual category Predicted Category Primary Non severe SevereTraining Data Primary 11 0 0 Non Severe 0 7 0 Severe 0 0 12Validation Data Primary 99 0 0 Non Severe 0 63 0 Severe 0 0 108Table 4: Confusion Matrix2.2.5 Variable Importance TableThe variable importance table gives a ranking of the overall importance of the predictor variables.SL.NO VARIABLES IMPORTANCE1 Excessive thirst 71.7342 Urinary Infection 34.5093 Wrinkles 33.0834 Skin problems 33.
0835 Fatigue 30.5846 Hunger 10.5677 Hayfever 8.7318 yeast infections 0.566 Table 5: Variables of ImportanceImportance scores are computed by using information about how variables were used as primary splitters and also as surrogate splitters. If a primary splitter is slightly better than a surrogate, then the primary splitter may “mask” the significance of the other variable. By considering surrogate splits, the importance measure calculated by giving more accurate measure of the actual and potential value of a predictor 21. To get the most accurate measure of importance, the user should select the option “Always compute surrogate predictors” on the Missing Data property page.
The importance score for the most important predictor is scaled as 71.734. Other predictors will have lower scores. Only predictors with scores greater than zero. III.
Results and DiscussionBased on the data set, the diagnosis was made by a physician with training and qualifications in diabetologists. For the purpose of this study, children and teenagers with IDDM and atopic dermatitis are excluded. Standard treatment is advised for the children and teenagers which is the same as children and teenagers with IDDM. The physician’s interpretation of clinical data and clinical images are stored in the medical databases22 23. An expert medical knowledge and specialized learning techniques to understand the meaning of unstructured data explanation.
The dataset contains 300 patients’ objects. The technique has been employed to categorize the patients and their symptoms using predictive modeling software and it evaluates based on the errors occurred in the classification, which is shown in table 6.SL.No Types of Error Value1. Mean Absolute Error 0.
01252. Root Mean Squared Error 0.01333. Relative Absolute Error 0.53624. Root Relative Squared Error 0.7089 Table 6: Error CalculationHere the main notified errors are root mean squared and mean absolute error, which are minimum in this model. This reveals that the model classifies the dataset perfectly.
The finding divulges that densely populated group of patients with close similarities based on the stages of IDDM are classified.IV. CONCLUSIONIn this Paper, popular learning methods are used to predict the patient information.
In Convolutional neural network technique, the three stages are identified as primary, non severe and severe8. It divulges that there is a perfect classification. Standard treatment is advised for the IDDM affected patients 3 especially for adults (;20).
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35-4323MichaelA. Pfeifer, Daniel Cook, Joel Brodsky, David Tice, A Reenan, Sally Swedine, JeffreyB.Halter, Daniel Porte”Quantitative evaluation of cardiac parasympathetic activity in normal and diabetic man Diabetes, 31 (4) (1982), pp. 339-345About the Authors:C.LALITHA is working as an Assistant Professor in the Department of Computer Application, Tagore College Arts and Science, Chrompet, Chennai. She has published many research articles in the National/International conferences and journals.
Her research interests include Learning Techniques, Data Mining.P. ANBUMANI is working as an Assistant Professor in the Department of Computer Application, Tagore College Arts and Science, Chrompet, Chennai. He has Pursuing Part-Time PhD in Department of Computer Science, Periyar University, Salem.
He has published many research articles in the National conferences. Her research interests include Learning Techniques.
